Closed asmaatamna closed 4 years ago
What we are interested in is IMHO rather Delta f / ||Delta x||
compared to sum(g+) / ||Delta x||
.
Then plotting the (cumulative) distribution of f/sum(g+)
over a decently large set of points sampled close to the constrained optimum should be enough, right?
Then plotting the (cumulative) distribution of f/sum(g+) over a decently large set of points sampled close to the constrained optimum should be enough, right?
Not quite, we must use Delta f = f(x) - f(x_opt)
, not f
. I don't see a good reason to not also look at the nominator and/or denominator alone (as it isn't really more difficult to do). Just for getting to know the absolute values we are actually facing it seems worth while.
You are right with f(x) - f(x_opt)
. I only thought to skip the individual (normalized) f
and sum(g+)
such that we do not need to calculate the distances to the optimum. But, yes you are right, this should not be more complicated.
@asmaatamna , can you remind me where I can see the above considered plotted distributions?
@nikohansen here: http://www.cmapx.polytechnique.fr/%7Easma.atamna/ppdata/ECDF_issue_1728/
23, 16, 9 are roughly the counts where f dominates, both are equal, and g dominates. That looks OK to me.
Also an outdated issue that can be closed.
Upon investigation of new data for
fmincon
, where we recordf+sum(g+)
(see here: http://www.cmapx.polytechnique.fr/~asma.atamna/ppdata-new/), and now the algorithm solves all the problems. Additionally, the number of constraints does not affect the performance.Possible debugging idea: increase the factor in front of
sum(g+)
(say100
) and rerun testsWe decided that it would be a good idea to have ECDFs of "sensitivity" data, where we plot
f
-values andsum(g+)
values for points around the optimum of the constrained problems (or even the ratio of these quantities).